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Assessment of structural sensitivity on the basis of artificial neural networks
Author(s) -
Pannier Stephan,
Graf Wolfgang
Publication year - 2010
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201010095
Subject(s) - sensitivity (control systems) , artificial neural network , computer science , reliability (semiconductor) , basis (linear algebra) , preprocessor , algorithm , artificial intelligence , mathematics , engineering , electronic engineering , physics , power (physics) , geometry , quantum mechanics
In engineering practice, the assessment of sensitivity is utilized to detect influential parameters in order to facilitate subsequent numerical simulation techniques. As sensitivity analyses are preprocessing methods for sophisticated numerical simulation techniques, e.g. reliability based optimization procedures, their application is always linked to an increase of the computational expense. In result, it is reasonable to couple sensitivity analysis and artificial neural networks (ANN). Therefore, multi‐faceted global sensitivity measures (GSM) may be formulated, taking advantage of different characteristics of the ANNs. Additionally, to take into account nonlinearities of the response surface, a new approach of sectional global n sensitivity measures is introduced. Generally, the sensitivity can be determined with $S_{i}=\hat{S}_{i}/\sum\limits_{j=1}^{n}\hat{S}_{j}$ . Thereby, $S_{i}$ denotes the sensitivity of interest and $\hat{S}_{i}$ a characteristic of the function $f:\mathcal{R}^n\rightarrow\mathcal{R}$ under investigation. This can be either the response $f$ itself or the first partial derivative thereof $\partial_{i}f$ . (© 2010 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)